Search

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions. [ABSTRACT FROM AUTHOR]

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014-December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy. [ABSTRACT FROM AUTHOR]

Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the "optimal" estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the "optimal" estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM2.5, PM10, NO2, and SO2 in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O3 slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area. [ABSTRACT FROM AUTHOR]

Different aerosol types exhibit distinct radiative effects in different regions, attributed to their unique optical characteristics and regional distributions. This study focuses on North China, which is impacted by both natural and anthropogenic aerosols with high concentrations and a variety of aerosol types. While many studies on aerosol direct radiative effects have been conducted in this region, the majority have focused on a specific type of aerosol or overall aerosol, leaving limited research on the direct radiative effects and contributions of different aerosol types. In this study, we use CALIPSO satellite data from 2011 to 2020 to investigate concentrations and distributions of different aerosol types. The results reveal that dust, polluted dust, polluted continental/smoke, and elevated smoke are the dominant aerosol types in North China. Based on the radiative closure experiment, we systematically calculate the radiative effects of different aerosol types and their corresponding contributions to the energy budget by combining satellite data with the Fu–Liou radiative transfer model. The annual average net aerosol direct radiative effect (ADRE) of North China is −6.1 and −13.43 W m−2 at the TOA and surface, respectively, causing a net warming effect of 7.33 W m−2 in the atmosphere. For each main aerosol type, dust contributes 93% to the shortwave ADRE in the western dust source region, while polluted dust mainly contributes 31% and 45% of the total ADRE, in Northwest China and North China Plain, respectively. Anthropogenic pollutant aerosols account for 58% of the total ADRE in Northeast China. This study holds great significance in elucidating the dominant aerosol types and their concentrations in North China, comprehending the impacts of different aerosol types on the local energy balance. [ABSTRACT FROM AUTHOR]

Top-down observational constraints on emissions flux estimates from satellite observations of chemical composition are subject to biases and errors stemming from transport, chemistry and prior emissions estimates. In this context, we developed an ensemble data assimilation system to optimize the initial conditions for carbon monoxide (CO) and aerosols, while also quantifying the respective emission fluxes with a distinct attribution of anthropogenic and wildfire sources. We present the separate assimilation of CO profile v9 retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument and Aerosol Optical Depth (AOD), collection 6.1, from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. This assimilation system is built on the Data Assimilation Research Testbed (DART) and includes a meteorological ensemble to assimilate weather observations within the online Community Atmosphere Model with Chemistry (CAM-chem). Inversions indicate an underestimation of CO emissions in CAMS-GLOB-ANT_v5.1 in China for 2015 and an overestimation of CO emissions in the Fire INventory from NCAR (FINN) version 2.2, especially in the tropics. These emissions increments are consistent between the MODIS AOD and the MOPITT CO-based inversions. Additional simulations and comparison with in situ observations from the NASA Atmospheric Tomography Mission (ATom) show that biases in hydroxyl radical (OH) chemistry dominate the CO errors. [ABSTRACT FROM AUTHOR]

Abstract Multiangle Implementation of Atmospheric Correction (MAIAC) is a new aerosol algorithm developed to retrieve aerosol optical depth (AOD) over land using the time series data to dynamically isolate aerosol and land contributions. However, there are still no comprehensive research on the quality of MAIAC AOD over China. In this paper, 1 km MAIAC AOD over China were examined against ground-based measurements to evaluate the performance of the data. In general, Aqua and Terra MAIAC retrievals have a high correlation coefficient (0.924 for Aqua and 0.933 for Terra) with ground-based observations and there are more than 72% of retrievals falling within the Expected Errors (EE = ±(0.05 + 0.2*AOD)). We found that the accuracy of MAIAC AOD is related with the AOD magnitude, aerosol size, seasons and surface types. In spring and summer, the AOD bias was influenced by both aerosol model and surface reflectance estimation. We also found that the aerosol model is the main source of AOD bias over desert regions. These results indicated that the MAIAC AOD could be used as a new aerosol data source for air quality and climate studies in China. Highlights • A comprehensive research was conducted to evaluate the performance of MAIAC aerosol products over China. • The accuracy of MAIAC AOD is related with the AOD magnitude, aerosol size, seasons, and surface types. • Good performance of Aqua and Terra MAIAC retrievals are shown. • The MAIAC AOD bias is mainly influenced by aerosol model in desert regions. [ABSTRACT FROM AUTHOR]

Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China's surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was >0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m3, and the maximum decrease reached 90.51 μg/m3. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a 'northeast–southwest' spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m3 in 2015 to 46.75 μg/m3 in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale. [ABSTRACT FROM AUTHOR]

The four wide-field-of-view (WFV) cameras aboard the GaoFen-1 (GF-1) satellite launched by China in April 2013 have been applied to the studies of the atmospheric environment. To highlight the advantages of GF-1 data in the atmospheric environment monitoring, an improved deep blue (DB) algorithm using only four bands (visible–near infrared) of GF-1/WFV was adopted to retrieve the aerosol optical depth (AOD) at ~500 m resolution in this paper. An optimal reflectivity technique (ORT) method was proposed to construct monthly land surface reflectance (LSR) dataset through converting from MODIS LSR product according to the WFV and MODIS spectral response functions to make the relationship more suitable for GF-1/WFV. There is a good spatial coincidence between our retrieved GF-1/WFV AOD results and MODIS/Terra or Himawari-8/AHI AOD products at 550 nm, but GF-1/WFV AOD with higher resolution can better characterized the details of regional pollution. Additionally, our retrieved GF-1/WFV AOD (2016–2019) results showed a good agreement with AERONET ground-based AOD measurements, especially, at low levels of AOD. Based on the same LSR dataset transmitted from 2016–2018 MODIS LSR products, RORT of 2016–2018 and 2019 GF-1/WFV AOD retrievals can reach up to 0.88 and 0.94, respectively, while both of RMSEORT are smaller than 0.13. It is indicated that using the ORT method to deal with LSR information can make GF-1/WFV AOD retrieval algorithm more suitable and flexible. [ABSTRACT FROM AUTHOR]

The recently released Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 introduced a fine scale aerosol optical depth (AOD) distribution, the 3 km product, which is expected to perform well in analyzing aerosols and identifying local air pollution, especially in the severely polluted atmosphere of China. However, few detailed evaluations of regional variations have been conducted. In this paper, we evaluate MODIS 3 km and 10 km AOD products for China against ground-based measurements and compare their performance with respect to spatial and temporal variations. The ground validations indicate that the two products are generally correlated well to ground-based observations. Spatially, the 3 km product slightly outperform the 10 km product in well-developed areas of southern China. Temporally, both products perform worse during spring and summer. Atmospheric clouds and underlying surface are two key factors that influence the accuracy and number of retrievals for both products. The comparison analysis reveals the newly introduced AOD product clearly shows good relationships with the coarse resolution retrievals in spatial and temporal variation but significant differences regarding details. The 3 km AOD product provides better aerosol gradients, more retrievals in bare areas of western China and some spikes of diurnal variation in cloudy days. Seasonal comparisons show the 3 km AOD product is higher than the 10 km product in all seasons, especially during spring and summer. Although the 3 km product for China generally performs slightly worse than the 10 km product, the added information of the MODIS 3 km AOD product shows potential for studying local aerosol characterization, and may facilitate studies of air pollution. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products are used worldwide for their reliable accuracy. However, the aerosol optical depth (AOD) usually retrieved by the operational dark target (DT) algorithm of MODIS has been missing for most of the urban regions in Central China. This was due to a high surface reflectance and heavy aerosol loading, especially in winter, when a high cloud cover fraction and the frequent occurrence of haze events reduce the number of effective satellite observations. The retrieval of the AOD from limited satellite data is much needed and important for further aerosol investigations. In this paper, we propose an improved AOD retrieval method for 500 m MODIS data, which is based on an extended surface reflectance estimation scheme and dynamic aerosol models derived from ground-based sun-photometric observations. This improved method was applied to retrieve AOD during heavy aerosol loading and effectively complements the scarcity of AOD in correspondence with urban surface of a higher spatial resolution. The validation results showed that the retrieved AOD was consistent with MODIS DT AOD (R = ~0.87; RMSE = ~0.11) and ground measurements (R = ~0.89; RMSE = ~0.15) from both the Terra and the Aqua satellite. The method can be easily applied to different urban environments affected by air pollution and contributes to the research on aerosol. [ABSTRACT FROM AUTHOR]

Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM2.5. However, the spatiotemporal relationship between AODs and PM2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM2.5 concentration across China and its 14 representative regions during 2016–2018 using the correlation coefficient (R), the PM2.5/AOD ratio (η), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM2.5 (R > 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R > 0.5) between the PM2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM2.5 per unit of these AODs (η > 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q > 0.4) of PM2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R > 0.5, q > 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM2.5 concentration based on AOD in different regions of China. [ABSTRACT FROM AUTHOR]

Although the Weather Research and Forecasting model with solar extensions (WRF-Solar) is tailed for solar energy applications, its official version lacks the consideration of the online aerosol-radiation process. To overcome this limitation, we have coupled the aerosol module online with the radiation module, then assimilated the high-resolution aerosol optical depth (AOD) from the Himawari-8 next-generation geostationary satellite using a three-dimensional variational (3DVAR) AOD data assimilation system to optimize the irradiance predictions with the better aerosol–radiation interaction. The results show that data assimilation can significantly eliminate the AOD underestimations and reasonably reproduce the AOD temporal distributions, improving 51.63% for biases and 61.29% for correlation coefficients. Compared with the original WRF-Solar version, coupled online with an advanced aerosol module minifies the bias value of global horizontal irradiance (GHI) up to 44.52%, and AOD data assimilation contributes to a further reduction of 17.43%. [ABSTRACT FROM AUTHOR]

In this study, the relationship of the surface shortwave radiation (SSR) with the daily precipitation intensity (DPREI), total cloud cover (TCC), and aerosol optical depth (AOD) is analyzed for the five northwestern provinces (Xinjiang, Qinghai, Gansu, Ningxia, Shaanxi) of China. It reveals the influences of the different DPREI levels and different TCC and AOD levels on the SSR diurnal variation. The results show that the average SSR is stronger in Xinjiang and Qinghai, and the seasons with the stronger intensity of SSR and larger variation of diurnal SSR in the provinces of northwest China are summer and spring. Detailed analysis shows that the influence of DPREI, TCC, and AOD on SSR is greater in the eastern region of northwest China than that in the western region. In spring and summer, it is possible to select a factor among DPREI, TCC, and AOD which has the best-fitting relationship with SSR in each province of northwest China. When considering the DPREI, TCC, and AOD simultaneously, the fitting effect of SSR is better than that of the results fitted by the single factor. In particular, the fitting ability of SSR in Xinjiang and Qinghai has been significantly improved. According to the basic meteorological elements such as DPREI, TCC, and AOD, the variation characteristics of regional mean SSR under typical weather conditions can be obtained. It is helpful to assess the SSR simply just based on the weather information from the meteorological office. [ABSTRACT FROM AUTHOR]

Multi-angle polarization measurement is an important technical means of satellite remote sensing applied to aerosol monitoring. By adding angle information and polarization measurements, aerosol optical and microphysical properties can be more comprehensively and accurately retrieved. The accuracy of aerosol retrieval can reflect the advantages and specific accuracy improvement of multi-angle polarization. In this study, the Multi-angle Imaging SpectroRadiometer (MISR) V23 aerosol products and the Polarization and Directionality of the Earth's Reflectance (POLDER) GRASP "high-precision" archive were evaluated with the Aerosol Robotic Network (AERONET) observations over China. Validation of aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD), and the Ångström exponent (AE) properties was conducted. Our results show that the AOD inversion accuracy of POLDER-3/GRASP is higher with the correlation coefficient (R) of 0.902, slope of 0.896, root mean square error (RMSE) of 0.264, mean absolute error (MAE) of 0.190, and about 40.71% of retrievals within the expected error (EE, ±   0.05 + 0.2 × A O D A E R O N E T ) lines. For AAOD, the performance of two products is poor, with better results for POLDER-3/GRASP data. POLDER-3/GRASP AE also has higher R of 0.661 compared with that of MISR AE (0.334). According to the validation results, spatiotemporal distribution, and comparison with other traditional scalar satellite data, the performance of multi-angle polarization observations is better and is suitable for the retrieval of aerosol properties. [ABSTRACT FROM AUTHOR]

The long-range transport of biomass burning pollutants from Southeast Asia has a significant impact on air quality in China. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) fire data and aerosol optical depth (AOD) products and the Tropospheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data were used to analyze the impact of air pollution caused by biomass burning in Southeast Asia on southern China. Results showed that Yunnan, Guangdong and Guangxi were deeply affected by biomass burning emissions from March to April during 2016–2020. Comparing the data for fires on the Indochinese Peninsula and southern provinces of China, it is obvious that the contribution of pollutants emitted by local biomass burning in China to air pollution is only a small possibility. The distribution of CO showed that the overall emissions increased greatly from March to April, and there was an obvious transmission process. In addition, the MODIS AOD in areas close to the national boundary of China is at a high level (>0.6), and the AOD in the southwest of Guangxi province and the southeast of Yunnan Province is above 0.8. Combined with a typical air pollution event in southern China, the UVAI combined with wind direction and other meteorological data showed that the pollutants were transferred from the Indochinese Peninsula to southern China under the southwest monsoon. The PM2.5 data from ground-based measurements and backward tracking were used to verify the pollutant source of the pollution event, and it was concluded that the degree of pollution in Yunnan, Guangxi and Guangdong provinces was related to the distance from the Indochinese Peninsula. Results indicate that it is necessary to carry out in-depth research on the impact of cross-border air pollution transport on domestic air quality as soon as possible and to actively cooperate with foreign countries to carry out pollution source research and control. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization lead to severe air pollution in China, threatening public health. However, it is challenging to understand the pollutants' spatial distributions by relying on a network of ground-based monitoring instruments, considering the incomplete dataset. To predict the spatial distribution of fine-mode particulate matter (PM2.5) pollution near the surface, we established models based on the back propagation (BP) neural network for PM2.5 mass concentration in North China using remote sensing products. According to our predictions, PM2.5 mass concentrations are affected by changes in surface reflectance and the dominant particle size for different seasons. The PM2.5 mass concentration predicted by the seasonal model shows a similar spatial pattern (high in the east but low in the west) influenced by the terrain, but shows high value in winter and low in summer. Compared to the ground-based data, our predictions agree with the spatial distribution of PM2.5 mass concentrations, with a mean bias of +17% in the North China Plain in 2017. Furthermore, the correlation coefficients (R) of the four seasons' instantaneous measurements are always above 0.7, indicating that the seasonal models primarily improve the PM2.5 mass concentration prediction. [ABSTRACT FROM AUTHOR]

The UV attenuation due to atmospheric aerosols in Guangzhou was quantitatively assessed using surface ultraviolet radiation (UV, 295-385 nm) observation, sun photometer and radiation models. Observations showed that the annual average value of aerosol optical depth (AOD) was 1.19 in UV spectral region of 340 nm, the annual average occurrence frequency of aerosol optical depth AOD>1.0 was 55%, and the annual average attenuation rate of surface UV direct radiation of 340 nm was 68%. It was proven in the observation of surface UV radiation and model evaluation that annual average attenuation of UV due to atmosphere was 75%, and that reached 72% in the dry season (October, November, December and January); while average attenuation of UV due to atmospheric aerosols reached 62% in the dry season. It was indicated that very significant UV attenuations due to atmospheric aerosols existed in Guangzhou urban agglomeration, and at least half of UV radiation was attenuated due to atmospheric aerosols. Such large-amplitude attenuation will have a significant impact on urban ecosystem and species chemical cycles, especially photochemical reaction processes. [ABSTRACT FROM AUTHOR]

Abstract: A seven-year analysis of clear-sky column-integrated aerosol optical properties and direct radiative forcing at Gosan in Korea is presented in this paper; the Gosan site is heavily impacted by aerosols emitted from the Asian continent. MODIS and AERONET Sun/sky radiometer measurements showed significant seasonal and geographical variability in aerosol optical depth (AOD), with high AODs over the east coastal industrialized regions of China in spring and summer. At the Gosan site, the monthly mean AOD at 675nm ranged between 0.12 and 0.36. High AODs were observed from April to June (>0.33), while low-to-moderate monthly mean AODs were apparent during the remaining months. The single scattering albedo (SSA) exhibited relatively low values from February to May (<0.93), indicating the presence of light-absorbing aerosols, whereas high values of SSA (>0.93) were observed during the remaining months. The annual average clear-sky direct forcing (forcing efficiency) at the surface was −27.55±9.21W m−2 (−91.85±11.12W m−2 τ −1) and at the top of the atmosphere (TOA) was −15.79±4.44W m−2 (−53.76±6.70W m−2 τ −1), thereby leading to an atmospheric absorption of 11.76±5.82W m−2. From March to June, the surface aerosol radiative forcing (forcing efficiency) ranged between −27.29 (–85.33) and −34.76W m−2 (−97.19W m−2 τ −1), whereas at TOA it ranged between −16.84 (−51.82) and −19.10W m−2 (−56.05W m−2 τ −1), and the atmospheric forcing ranged between 10.45 and 16.41W m−2. The atmospheric forcing (i.e., atmospheric absorption) caused an increase in atmospheric heating (about 1.5 to 3.0K day−1). The strongest radiative heating was observed from April to May (>2.5K day−1). [Copyright &y& Elsevier]

China experienced heavy aerosol pollution in recent years. The aerosol pollution changes surface solar radiation and affects the land terrestrial carbon and water cycles in China. However, there are still some uncertainties in evaluating aerosol effects on ecosystem. Therefore, we combined the MODIS Atmosphere and Land products with process-based model to examine the sensitivity of gross primary productivity (GPP) and water use efficiency (WUE) to AOD. We also evaluated the effects of aerosol loadings on GPP and WUE at China FLUX sites from 2002 to 2015. Our results indicated that the radiative effects of aerosols on GPP varied with the vegetation type, which is consistent with other studies. Aerosol enhances the GPP by 2% in forest and reduces the GPP by 7% in grass land during the study period. We also found that the radiative effects of aerosols on WUE varied with the vegetation type. The WUE was promoted by the aerosols for all surface types when AOD is lower than 2.3. This indicates that the aerosol could enhance the plants WUE in most cases. Cloud during the study period restrains aerosol effects on growth of plants and improves the aerosol effects on WUE. Aerosol loadings during the study period fertilized the growth in two forest growth and slightly impede the growth of grass. They also improve the WUE for all vegetation types. The WUE increases more that 4% in most of sites increases. The results in this paper could help to fully understand the influences of aerosol on land carbon and water cycle. • MODIS products and BESS model were used to evaluate aerosol effects on GPP and WUE. • Aerosol enhances the GPP by 2% in forest during the study period. • The WUE increases more than 4% in most of sites due to the aerosol effects. • Cloud restrains aerosol effects on GPP and improves aerosol effects on WUE. [ABSTRACT FROM AUTHOR]

Based on the analysis of solar radiation and meteorological parameters measured at a subtropical forest in China during 2013–2016, a new empirical model of global solar irradiance has been developed. It can calculate global solar irradiance at the ground and at the top of the atmosphere (TOA); both are in agreement with the observations. This model is used to calculate the extinction of global solar irradiance in the atmosphere and the contributions from absorbing and scattering substances. The loss of global solar irradiance is dominated by absorbing and absorbing substances. The results show clear seasonal and interannual variations during the observation period. Sensitivity analysis indicates that global solar irradiance is more sensitive to changes in scattering, quantified by the S/G factor (S and G are diffuse and global solar radiation, respectively), than to changes in absorption. The relationships between the extinction factor (AF) of G and S/G and between the AF and the aerosol optical depth (AOD) are determined and used to estimate S/G and the AOD from the measured AF. This empirical model is applied to calculate the albedos at the TOA and the ground. This empirical model is useful to study global solar radiation and the energy–atmosphere interactions. [ABSTRACT FROM AUTHOR]

In the present paper, we have analyzed the spatiotemporal distributions and trends of aerosol optical depth observed at 550 nm (AOD 550), and its relation with the optical properties of clouds over East China. For this purpose, we have used the long-term (2000–2017) data obtained from the Moderate-resolution Imaging Spectroradiometer (MODIS) Terra satellite to examine and understand the existing relationship between them. The spatial gradient of AOD 550 showed an increase from the South to the North of the study domain. However, the seasonal variation of AOD 550 was found high in summer and low during the winter. We have noticed a significant increasing trend in AOD over the northern part of the study area, especially in autumn and winter seasons. The study also investigated the spatial and temporal changes to understand the relationship between AOD and cloud properties, namely cloud fraction (CF), cloud top temperature (CTT), Cloud effective radius (CER) and cloud top pressure (CTP), respectively. Besides, the linear regression analysis was conducted to estimate the time series trend for the aerosol and cloud properties during the study period. Further, the regional correlation maps were adopted to improve our understanding of the existing relationship between aerosol and cloud properties. A positive correlation between AOD and CF and CER is evident in most of the places in East China. Image 1 • Aerosol and cloud properties exhibited substantial spatial and temporal variabilities in the recent years over East China. • Significant increasing (decreasing) trend in AOD (CTT and CTP) was found over the Shandong Province in the North of the study domain. • Moderate to high positive correlations were noticed between AOD and CER attributed to the dynamics played by the meteorology. [ABSTRACT FROM AUTHOR]

High spatial resolution estimating of exposure to particulate matter 2.5 (PM2.5) is currently very limited in China. This study uses the newly released nationwide, hourly PM2.5 concentrations to create a nationwide, geographically weighted regression (GWR) model to estimate ground-level PM2.5 concentrations in China. A3 km resolution aerosol optical depth (AOD) product from MODIS is used as the primary predictor. Fire emissions detected by MODIS fire count were considered in the model development process. Additionally, meteorological features were used as covariates in the model to improve the estimation of ground-level PM2.5 concentrations. The model performed well and explained 81% of the daily PM2.5 concentration variations in model predictions, and the cross validations R² is 0.79. The cross-validated root mean squared error (RMSE) of the model was 18.6 μg/m³.Annual PM2.5 concentrations retrieved by the MODIS 3 km AOD product indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. Estimated high-resolution national-scale daily PM2.5 maps are useful to identify severe air pollution episodes and determine health risk assessments. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions, especially for regions without PM monitoring sites. [ABSTRACT FROM AUTHOR]

Due to the significant spatial variation of the performance of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (AOD) retrievals, validation is very important for applications of MODIS AOD products at regional scales. This study presents a comparative analysis of Collection 6.1 MODIS AOD retrievals and ground measurements from five local sites and one Aerosol Robotic Network (AERONET) site in the Yangtze River Delta (YRD) region, which significantly complements the previous validation that utilized limited AERONET measurements. Generally, MODIS AOD retrievals showed a reasonable agreement with collocated ground measurements (R2 > 0.7), with 66% of Dark Target (DT) 10 km retrievals, 56% of Deep Blue (DB) 10 km retrievals, and 69% of DT 3 km retrievals falling within the expected error (EE = ±(0.05 + 0.2 × AOD)). Nevertheless, it was found that the DT AOD retrievals tended to be overestimated over urbanized and lakeside sites, while the DB AOD retrievals tended to be underestimated over all ground sites except for lakeside sites. Such patterns appeared to be linked with the systematic biases of the single-scattering albedo estimation in the AOD retrieval algorithms. Another significant finding of this study is that the uncertainties of the MODIS AOD retrievals were highly correlated with the land cover proportions of urbanized features and water (LCP_UW) in the surrounding region, especially for the DT products. An empirical correction method based on these correlations could substantially reduce the uncertainties of DT AOD products over high LCP_UW areas. The results not only highlight the significant impacts of both urban and water areas on the MODIS AOD retrieval algorithms but also create new possibilities to correct such impacts once the universal correlations between LCP_UW and the uncertainty measures are established. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM2.5 measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R2) increased by 0.05 ± 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 ± 1.0 μg/m3, 8 ± 3%, and 1.40 ± 0.2 μg/m3, respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R2 values of the validation dataset using CapsNet improved by 4 ± 2%, and RMSE, MRE, and MAE decreased by 1.50 ± 0.4 μg/m3, ~5%, and 0.60 ± 0.2 μg/m3, respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R2 values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study proposed the CapsNet model to estimate PM2.5 concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants. [ABSTRACT FROM AUTHOR]

Aerosol optical depths (AODs) from MODIS and MISR onboard the Terra satellite are assessed by comparison with measurements from four AERONET sites located in northern China for the period 2006-2009. The results show that MISR performs better than MODIS at the SACOL and Beijing sites. For the Xianghe and Xinglong sites, MODIS AOD retrievals are better than those of MISR. Overall, the relative error of the Angstrom exponent from MISR compared with AERONET is about 14%, but the MODIS error can reach 30%. Thus, it may be better to use the MISR Angstrom exponent to derive wavelength-dependent AOD values when calculating the aerosol radiative forcing in a radiative transfer model. Seasonal analysis of AOD over most of China shows two main areas with high aerosol loading: the Taklimakan Desert region and the southern part of North China and northern part of East China. The locations of these two areas of high aerosol loading do not change with season, but the AOD values have significant seasonal variation. The largest AOD value in the Taklimakan appears in spring when the Angstrom exponents are the lowest, which means the particle radii are relatively large. Over North and East China, the highest aerosol loading appears in summer. The aerosol particles are smallest in summer over both high-AOD areas. [ABSTRACT FROM AUTHOR]

Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor. [ABSTRACT FROM AUTHOR]

Aerosols play an important role in climate change, and ground aerosols (e.g., fine particulate matter, abbreviated as PM2.5) are associated with a variety of health problems. Due to clouds and high reflectance conditions, satellite-derived aerosol optical depth (AOD) products usually have large percentages of missing values (e.g., on average greater than 60% for mainland China), which limits their applicability. In this study, we generated grid maps of high-resolution, daily complete AOD and ground aerosol coefficients for the large study area of mainland China from 2015 to 2018. Based on the AOD retrieved using the recent Multi-Angle Implementation of Atmospheric Correction advanced algorithm, we added a geographic zoning factor to account for variability in meteorology, and developed an adaptive method based on the improved full residual deep network (with attention layers) to impute extensively missing AOD in the whole study area consistently and reliably. Furthermore, we generated high-resolution grid maps of complete AOD and ground aerosol coefficients. Overall, compared with the original residual model, in the independent test of 20% samples, our daily models achieved an average test R2 of 0.90 (an improvement of approximately 5%) with a range of 0.75–0.97 (average test root mean square error: 0.075). This high test performance shows the validity of AOD imputation. In the evaluation using the ground AOD data from six Aerosol Robotic Network monitoring stations, our method obtained an R2 of 0.78, which further illustrated the reliability of the dataset. In addition, ground aerosol coefficients were generated to provide an improved correlation with PM2.5. With the complete AOD data and ground coefficients, we presented and interpreted their spatiotemporal variations in mainland China. This study has important implications for using satellite-derived AOD to estimate aerosol air pollutants. [ABSTRACT FROM AUTHOR]

Satellite aerosol optical depth (AOD) plays an important role for high spatiotemporal-resolution estimation of fine particulate matter with diameters ≤2.5 μm (PM2.5). However, the MODIS sensors aboard the Terra and Aqua satellites mainly measure column (integrated) AOD using the aerosol (extinction) coefficient integrated over all altitudes in the atmosphere, and column AOD is less related to PM2.5 than low-level or ground-based aerosol (extinction) coefficient (GAC). With recent development of automatic differentiation (AD) that has been widely applied in deep learning, a method using AD to find optimal solution of conversion parameters from column AOD to the simulated GAC is presented. Based on the computational graph, AD has considerably improved the efficiency in applying gradient descent to find the optimal solution for complex problems involving multiple parameters and spatiotemporal factors. In a case study of the Jing-Jin-Ji region of China for the estimation of PM2.5 in 2015 using the Multiangle Implementation of Atmospheric Correction AOD, the optimal solution of the conversion parameters was obtained using AD and the loss function of mean square error. This solution fairly modestly improved the Pearson's correlation between simulated GAC and PM2.5 up to 0.58 (test R2: 0.33), in comparison with three existing methods. In the downstream validation, the simulated GACs were used to reliably estimate PM2.5, considerably improving test R2 up to 0.90 and achieving consistent match for GAC and PM2.5 in their spatial distribution and seasonal variations. With the availability of the AD tool, the proposed method can be generalized to the inversion of other similar conversion parameters in remote sensing. [ABSTRACT FROM AUTHOR]